Capillary Extrusion Studies of LLDPE/LDPE Blends: Effects of Manufacturing Technology of LLDPE and Long Chain Branching
Bibliographic record
Abstract
Abstract The processing behavior of a number of LLDPE/LDPE blends with emphasis on the effects of manufacturing technology of LLDPE and long chain branching is presented. A single low-density polyethylene was blended with two Ziegler-Natta linear-low-density polyethylenes LLDPE's and two metallocene-LLDPE's having distinctly different molecular structures. The weight fractions of the LDPEs used in the blends were 1 wt.%, 5 wt.%, 10 wt.%, 20 wt.%, 50 wt.% and 75 wt.%. Capillary extrusion reveals that the onset of sharkskin and gross melt fracture are slightly influenced with the addition of LDPE into LLDPE. However, the amplitude of the oscillations in the stick-slip flow regime was found to scale well with the weight fraction of LDPE even at amounts as low as 1 wt.%. Furthermore, it was observed that the onset of this flow regime was shifted to higher shear rates with increase of LDPE content. Shear rheology was found to be insensitive to detect small levels of LDPE. On the other hand, extensional rheology was found to be capable of detecting levels of LDPE as low as 1 wt.% at high Hencky strain rates (typically greater than 5 s−1) although only for certain blends.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".